An exchange is a central marketplace with established rules and regulations where buyers and sellers meet to trade. Some exchanges, referred to as open outcry exchanges, operate using a trading floor where buyers and sellers physically meet on the floor to trade. Other exchanges, referred to as electronic exchanges, operate by an electronic or telecommunications network instead of a trading floor to facilitate trading in an efficient, versatile, and functional manner. Electronic exchanges have made it possible for an increasing number of people to actively participate in a market at any given time. The increase in the number of potential market participants has advantageously led to, among other things, a more competitive market and greater liquidity.
With respect to electronic exchanges, buyers and sellers may log onto an electronic exchange trading platform by way of a communication link through their user terminals. Once connected, buyers and sellers may typically choose which tradable objects they wish to trade. As used herein, the term “tradable object” refers to anything that can be traded with a quantity and/or price. It includes, but is not limited to, all types of traded events, goods and/or financial products, which can include, for example, stocks, options, bonds, futures, currency, and warrants, as well as funds, derivatives and collections of the foregoing, and all types of commodities, such as grains, energy, and metals. The tradable object may be “real,” such as products that are listed by an exchange for trading, or “synthetic,” such as a combination of real products that is created by the user. A tradable object could actually be a combination of other tradable object, such as a class of tradable objects.
Every day, there are thousands of traders buying and selling for many different reasons, such as, for example, fear of loss, hope of gain, hedging, broker recommendations, and many others. To profit in electronic markets, market participants must be able to assimilate large amounts of data in order to recognize market trends and to view current market conditions. However, trying to figure out why market participants are buying or selling can be very difficult. Chart patterns may put buying and selling activities into perspective by providing a concise picture of the two activities as a tool to analyze markets. Among many different market data types, traders may wish to view one or more price charts to forecast future price movements, for example. A price chart displays a sequence of prices plotted over a specific timeframe, as well as other information that can be useful in analyzing market trends and market patterns, including technical indicators, such as, for example, moving averages.
Typically, a timeframe used for forming a price chart depends on the level of data compression, and determines the level of detail of the displayed data. The timeframe of a price chart may be any timeframe, including, for example, intra-day, daily, weekly, monthly, quarterly, or annual. An intra-day chart may display open, close, high, and low prices for an identified interval, such as one or more minutes, or seconds during a trading day. Then, the daily chart may display a single set of price data for each day of trading depicted in the chart. The weekly chart is made up of daily data that has been compressed to show each week as a single point, and so forth.
Traders usually concentrate on charts made up of daily and intra-day data series to forecast short-term price movements, whereas weekly and monthly charts are typically used to spot long-term trends. While some traders may wish to view either a long-term chart or a short-term chart, many traders often want to view the combination of the two chart types to see the full picture of the market. Therefore, a preferable approach would be to show the two chart types on a single integrated graph. However, due to the screen size limitations, the prior art systems do not offer satisfactory solutions for viewing the short-term chart details and the long-term chart details on a single axis.
A typical display has the screen size on the order of 1024×768 pixels, which means that using the extreme minimum of one pixel per each value to be displayed on the screen, only 1024 values could be displayed on a single linear chart. For example, if typical one-minute bars with high, low, open, and close were used, the maximum number of values that could be shown would be 256, or a four and a quarter hour time period (using a minimal four pixels per bar, with three pixels used for each bar and one pixel used for spacing between the bars). Then, if five-minute bars were displayed, the maximum number of bars would be still 256, while the time period range would increase to twenty-one and a quarter hours of trading time.
Therefore, based on the examples given above, the longer the time range displayed, the less detail is available about the fine-grained movements of the market. FIG. 1A is a block diagram illustrating a time scale diagram 100 that is often used to display time data series. The time scale diagram 100 is a linear time scale chart that is wide enough to see the entire year of data. However, as shown in FIG. 1A, it is almost impossible to see anything as small as a day, and totally impossible to see anything on the order of an hour, a minute, or a second, since there is not enough room to show such level of detail. Thus, the linear time scale chart 100 does not meet the objectives of showing both historical context and recent time fine-grained detail.
FIG. 1B illustrates another approach that can be used to display the combination of current and historical time series data using multiple linear time scale graphs 150. FIG. 1B illustrates three linear time scale charts 152, 154, and 156 that provide both, the broad historical context and most recent time fine-grained detail. More specifically, the chart 152 displays weekly, monthly, and quarterly timeframes, while the chart 154 displays hourly, and daily timeframes, and finally, the chart 156 displays data on the order of minutes and seconds corresponding to the last hour. However, this approach of displaying data is extremely wasteful of space, and a group of only few such graphs can be displayed on a screen. Also, the use of multiple graphs adds to the cognitive workload on a user, since it is harder to see and recognize price movements or patterns relative to historical highs and lows.
Another approach to displaying time series data is to use a logarithmic time scale. One possible implementation of a logarithmic time scale graph is to mark the leftmost mark on the scale “1 second,” while the next evenly spaced mark to the right would be “10 seconds,” assuming that a base-10 logarithmic scale is used, the next evenly spaced mark would be “100 seconds” (1 minute 40 seconds), the next would be “1000 seconds” (16 minutes, 40 seconds), the next would be “10,000 seconds” (two-hours, forty-six minutes, forty seconds), and so on. This approach appears to allow for displaying a wide range of time data series. FIG. 2 is a block diagram illustrating a base-10 logarithmic scale 200. The example graphical scale 200 displays the natural time periods rather than the evenly spaced marks, where the leftmost mark on the scale represents a time of one second, the next mark represents one minute, the next mark represents one hour, and so on.
However, the logarithmic time scale approach suffers from several problems. First, the natural evenly-spaced divisions are not natural units that make sense to a user, like seconds, minutes, five minutes, hours, days, weeks, months, quarters, etc., but are mathematical powers of the smallest logarithmic base unit. This problem can be addressed by using more natural division marks, such as the one shown in relation to FIG. 2, although the illustrated marks are not regularly spaced. The second problem with the logarithmic time scale is that the meaning of “bar” data is not obvious for viewing when displayed in relation to the logarithmic scale, because each bar appearing at a different place on the time scale has a different rollup period, and therefore causes reading of the displayed data to be counter-intuitive.
Thus, it would beneficial to provide a graphical display of data series that will not only provide up-to-date details and historical context data, but also one that will be intuitive for a trader to use.